{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e67abf13-09f9-45ec-aa3d-42fa97763da5",
   "metadata": {},
   "source": [
    "# 第八章 思维骨架提示"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "592472ce-68ce-4c7f-8036-9b1b3d078175",
   "metadata": {},
   "source": [
    "<div class=\"toc\">\n",
    " <ul class=\"toc-item\">\n",
    "     <li><span><a href=\"#一、引言\" data-toc-modified-id=\"一、引言\">一、引言</a></span></li>\n",
    "     <li>\n",
    "         <span><a href=\"#二、思维骨架提示\" data-toc-modified-id=\"二、思维骨架提示\">二、思维骨架提示</a></span>\n",
    "         <ul class=\"toc-item\">\n",
    "             <li><span><a href=\"##2.1 骨架提示模板\" data-toc-modified-id=\"2.1 骨架提示模板\">2.1 骨架提示模板</a></span></li>\n",
    "             <li><span><a href=\"##2.2 要点展开模板\" data-toc-modified-id=\"2.2 要点展开模板\">2.2 要点展开模板</a></span></li>\n",
    "             <li><span><a href=\"##2.3 骨架提示和要点展开合并模板\" data-toc-modified-id=\"2.3 骨架提示和要点展开合并模板\">2.3 骨架提示和要点展开合并模板</a></span></li>\n",
    "             </ul>\n",
    "         </li>\n",
    "     <li><span><a href=\"#三、总结与讨论\" data-toc-modified-id=\"三、总结与讨论\">三、总结与讨论</a></span></li>\n",
    "     </ul>\n",
    "</div>"
   ]
  },
  {
   "attachments": {
    "8e470380-d236-4837-8710-98e7f2960851.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "id": "bd7ae329-4590-4b65-ad5b-aa6c63189d91",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 一、引言\n",
    "***\n",
    "\n",
    "**人类如何高效地回答问题？**我们并不总是按顺序思考问题，然后给出答案。相反，我们通常会首先根据一些策略为各种类型的问题构建一个思考的框架，然后在此基础上添加细节以进一步阐述。在提供咨询、参加考试或撰写论文等正式场合中，这一点尤其重要。我们能否让大语言模型（Large Language Models, LLMs）以这样的方式进行思考？\n",
    "\n",
    "**受人类思考和写作过程的启发**，清华和微软的研究者提出了「**思维骨架**」（Skeleton of Thought，SoT）。SoT 是以数据为中心优化推理效率的初步尝试，并展示了通过明确规划语言中的答案结构来获得高质量答案的潜力。相应的论文成功发表于 ICLR 2024 - [Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding](https://openreview.net/forum?id=mqVgBbNCm9)\n",
    "\n",
    "\n",
    "\n",
    "具体来说，我们可以引导 LLM 首先构建出一个思维骨架。在这个骨架的基础上，LLM 可以并行处理每个部分，从而提高处理速度。SoT 不仅可以用于加速开源模型的分批解码，还可以用于加速闭源模型的并行 API 调用。下面是思维骨架 (SoT) 的示意图。\n",
    "\n",
    "![2024 ICLR  SoT_思维骨架：大语言模型可进行并行解码 2.png](attachment:8e470380-d236-4837-8710-98e7f2960851.png)\n",
    "\n",
    "（1）SoT 并不是按顺序生成答案，而是并行地生成答案的不同部分。\n",
    "\n",
    "（2）更具体地说，当问题给定时，**SoT 会首先引导 LLM 构建思维骨架**，然后进行批量解码或并行 API 调用以并行扩展多个要点，最后汇总输出结果以得出最终答案。\n",
    "\n",
    "（3）需要注意的是，**目前的 SoT 适用于需要较长答案的问题，这些问题的结构可以提前规划，但不适用于需要逐步推理或只需要简短答案的问题**。\n",
    "\n",
    "本章将详细介绍思维骨架提示。我们将深入阐述关键原理，并提供实际的代码示例，以帮助读者全面理解并掌握思维骨架提示 的关键原理和实际应用。阅读本章后，读者将能够更专业、准确地理解和应用思维骨架提示。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ea03f97-55b2-4e5e-84d1-41b1d0b7ab55",
   "metadata": {},
   "source": [
    "## 二、思维骨架提示\n",
    "***\n",
    "\n",
    "注意：我使用的测试环境是 Anaconda + Python 3.8.10。Python 的版本要求 Python 3.7+。\n",
    "\n",
    "开始实验之前，我们需要安装好本次实验所需的第三方依赖库：`openai`：OpenAI Python 库提供了一个便捷的途径，让任何 Python 3.7+ 应用程序可以访问 OpenAI REST API。您可以通过运行以下命令来安装：`pip install openai==1.10.0`。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d2b18e9-d707-4bf1-bc21-180261ef5935",
   "metadata": {},
   "source": [
    "导入此次实验所需的依赖库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6161f2dc-84a1-4ce2-aaf2-be2999a1bfdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "from openai import OpenAI\n",
    "from concurrent.futures import ThreadPoolExecutor\n",
    "\n",
    "\n",
    "# 从环境变量中获取 OpenAI API Key 或者直接赋值\n",
    "OPENAI_API_KEY = os.environ['OPENAI_API_KEY']\n",
    "OPENAI_API_KEY = \"sk-...你的 OpenAI API Key\"\n",
    "\n",
    "# 如果您使用的是官方 API，就直接用 https://api.openai.com/v1 就行。\n",
    "BASE_URL = \"https://api.openai.com/v1\"\n",
    "# 如果您使用的不是官方 API，而是通过代理进行请求，请设置您的代理 URL。\n",
    "# BASE_URL = \"https://api.xxx.../v1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ddb67b03-9216-4e2d-93ef-d3b0f0e26724",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 实例化 OpenAI 对象\n",
    "# 传入参数：OpenAI API Key（必需）、Base URL 和最大重试次数\n",
    "client = OpenAI(api_key=OPENAI_API_KEY, base_url=BASE_URL, max_retries=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc0a7c50-c6d3-4dd3-923f-b62e4d81bf85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 参数 n，整数或 Null，可选项，默认为 1。为每条输入信息生成多少个聊天完成选项。\n",
    "# 参数 temperature，实数值或 Null，可选项，默认为 1。使用的采样温度，介于 0 和 2 之间。0.8 等较高值会使输出更加随机，而 0.2 等较低值会使输出更加集中和确定。\n",
    "\n",
    "def get_completions(llm_prompt, model_endpoint):\n",
    "\n",
    "    response = client.chat.completions.create(model=model_endpoint,\n",
    "                                              messages=[\n",
    "                                                        {\"role\": \"user\", \"content\": llm_prompt}\n",
    "                                                       ],\n",
    "                                              n=1, temperature=0.20, seed=42,\n",
    "                                              presence_penalty=0, frequency_penalty=0,\n",
    "                                              max_tokens=1024\n",
    "                                             )\n",
    "\n",
    "    return response.choices[0].message.content.strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f9060ef-31f9-4662-b11d-db7d1cc5c58b",
   "metadata": {},
   "source": [
    "### 2.1 骨架提示模板"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "397d171b-4402-40a4-852e-29da08ea3cd3",
   "metadata": {},
   "source": [
    "**骨架提示阶段（配有 2-shot 演示）。**我们编写骨架提示模板的目的是为了引导 LLM 输出简洁明了的答案骨架。这样，我们就可以从 LLM 的骨架响应中提取关键信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2c7acb3d-577f-4140-8537-41dd2ff524f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "生成的思维骨架-中文：\n",
      "1. 增强心肺功能。\n",
      "2. 控制体重。\n",
      "3. 提高免疫力。\n",
      "4. 改善睡眠质量。\n",
      "5. 减少患疾病风险。\n",
      "6. 提升心情和情绪。\n",
      "7. 延缓衰老。\n"
     ]
    }
   ],
   "source": [
    "llm = \"gpt-3.5-turbo-0125\"\n",
    "\n",
    "question_Chinese = \"经常锻炼有什么好处呢？\"\n",
    "\n",
    "skeleton_prompt_Chinese = f\"\"\"您是一位组织者，只负责提供回答问题的骨架（而非完整内容）。\\\n",
    "以要点列表（编号为 1.、2.、3.等）的形式提供骨架，以回答问题。\\\n",
    "每个要点应非常简短，只有 2∼8 个字，而不是写一个完整的句子。\\\n",
    "一般来说，骨架应包含 3∼10 个要点。\n",
    "\n",
    "问题：\n",
    "中国菜有哪些典型类型？\n",
    "骨架：\n",
    "1. 饺子。\n",
    "2. 面条。\n",
    "3. 点心。\n",
    "4. 火锅。\n",
    "5. 云吞。\n",
    "6. 麻婆豆腐。\n",
    "7. 叉烧。\n",
    "8. 炒饭。\n",
    "\n",
    "问题：\n",
    "对于个体有哪些减少碳排放的实用建议？\n",
    "骨架：\n",
    "1. 节约能源。\n",
    "2. 高效交通。\n",
    "3. 家庭能效。\n",
    "4. 减少用水量。\n",
    "5. 可持续饮食。\n",
    "6. 可持续的旅行。\n",
    "\n",
    "现在，请为下面的问题提供骨架。\n",
    "{question_Chinese}\n",
    "骨架：\n",
    "\"\"\"\n",
    "\n",
    "skeleton_result_Chinese = get_completions(skeleton_prompt_Chinese, llm)\n",
    "\n",
    "print(f\"生成的思维骨架-中文：\\n{skeleton_result_Chinese}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3f4f1ad6-d06d-4eaa-bc1e-beea38a23860",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "生成的思维骨架-英文：\n",
      "1. Weight management.\n",
      "2. Improved mood.\n",
      "3. Increased energy levels.\n",
      "4. Better sleep quality.\n",
      "5. Stronger immune system.\n",
      "6. Reduced risk of chronic diseases.\n",
      "7. Improved cognitive function.\n",
      "8. Better overall physical health.\n"
     ]
    }
   ],
   "source": [
    "llm = \"gpt-3.5-turbo-0125\"\n",
    "\n",
    "question_English = \"What are the benefits of regular exercise?\"\n",
    "\n",
    "skeleton_prompt_English = f\"\"\"You are an organizer responsible for only giving the skeleton (not the full content) for answering the question.\\\n",
    "Provide the skeleton in a list of points (numbered 1., 2., 3., etc.) to answer the question. \\\n",
    "Instead of writing a full sentence, each skeleton point should be very short with only 2∼6 words. \\\n",
    "Generally, the skeleton should have 3∼10 points.\n",
    "\n",
    "Question:\n",
    "What are the typical types of Chinese dishes?\n",
    "Skeleton:\n",
    "1. Dumplings.\n",
    "2. Noodles.\n",
    "3. Dim Sum.\n",
    "4. Hot Pot.\n",
    "5. Wonton.\n",
    "6. Ma Po Tofu.\n",
    "7. Char Siu.\n",
    "8. Fried Rice.\n",
    "\n",
    "Question:\n",
    "What are some practical tips for individuals to reduce their carbon emissions?\n",
    "Skeleton:\n",
    "1. Energy conservation.\n",
    "2. Efficient transportation.\n",
    "3. Home energy efficiency.\n",
    "4. Reduce water consumption.\n",
    "5. Sustainable diet.\n",
    "6. Sustainable travel.\n",
    "\n",
    "Now, please provide the skeleton for the following question.\n",
    "{question_English}\n",
    "Skeleton:\n",
    "\"\"\"\n",
    "\n",
    "skeleton_result_English = get_completions(skeleton_prompt_English, llm)\n",
    "\n",
    "print(f\"生成的思维骨架-英文：\\n{skeleton_result_English}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f0efdbe-a7da-4128-8927-5d2f39733816",
   "metadata": {},
   "source": [
    "### 2.2 要点展开模板"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "810c65f2-8203-487d-a508-a064c2d36965",
   "metadata": {},
   "source": [
    "要点扩展阶段。我们基于得到的骨架，让 LLM 在每个要点上进行**并行扩展**。最后，当所有要点完成后，我们将各点的扩展响应连接起来，形成最终答案。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9531908d-0d92-4139-a1f5-81a7ef61b1cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('1', 'Weight management.'), ('2', 'Improved mood.'), ('3', 'Increased energy levels.'), ('4', 'Better sleep quality.'), ('5', 'Stronger immune system.'), ('6', 'Reduced risk of chronic diseases.'), ('7', 'Improved cognitive function.'), ('8', 'Better overall physical health.')]\n",
      "[('1', '增强心肺功能。'), ('2', '控制体重。'), ('3', '提高免疫力。'), ('4', '改善睡眠质量。'), ('5', '减少患疾病风险。'), ('6', '提升心情和情绪。'), ('7', '延缓衰老。')]\n"
     ]
    }
   ],
   "source": [
    "# 我们可以使用一个专门设计的正则表达式 \"(\\d+)\\.\\s?([\\s\\S]+?)(?=\\n|\\n*$)\" 从骨架响应中提取要点索引和要点骨架。\n",
    "key_point_skeleton_English = re.findall(r\"(\\d+)\\.\\s?([\\s\\S]+?)(?=\\n|\\n*$)\", skeleton_result_English)\n",
    "key_point_skeleton_Chinese = re.findall(r\"(\\d+)\\.\\s?([\\s\\S]+?)(?=\\n|\\n*$)\", skeleton_result_Chinese)\n",
    "print(key_point_skeleton_English)\n",
    "print(key_point_skeleton_Chinese)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c2404ee1-5b6a-4960-bd82-41db372e40cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def expand_point_English(point_data):\n",
    "    point_index, key_point = point_data\n",
    "    point_expanding_prompt = f\"\"\"You are responsible for continuing the writing of one and only one point \\\n",
    "in the overall answer to the following question.\n",
    "\n",
    "{question_English}\n",
    "\n",
    "The skeleton of the answer is:\n",
    "{skeleton_result_English}\n",
    "\n",
    "Continue and only continue the writing of point {point_index}. \\\n",
    "Write it accurately and concisely in 2∼4 sentences, and do not continue with other points!\n",
    "\"\"\"\n",
    "    point_expanding = get_completions(point_expanding_prompt, llm)\n",
    "\n",
    "    return (point_index, point_expanding)\n",
    "\n",
    "\n",
    "def expand_point_Chinese(point_data):\n",
    "    point_index, key_point = point_data\n",
    "    point_expanding_prompt = f\"\"\"您有责任在下面问题的总答案中续写一个且仅一个要点。\n",
    "\n",
    "{question_Chinese}\n",
    "\n",
    "答案的骨架是：\n",
    "{skeleton_result_Chinese}\n",
    "\n",
    "继续且仅继续书写点 {point_index}。用 4∼6 句话准确、简洁地写完，不要续写其他要点！\n",
    "\"\"\"\n",
    "    point_expanding = get_completions(point_expanding_prompt, llm)\n",
    "\n",
    "    return (point_index, point_expanding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "43bdf54b-bf28-4beb-b857-ea13f2812efd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "要点扩展后的内容-中文：\n",
      "1. 经常锻炼可以增强心肺功能，使心脏更加强壮，提高肺活量，增加氧气的吸收量。这有助于提高身体的耐力和抵抗力，减少疲劳感，让我们在日常生活中更加精力充沛和活力十足。\n",
      "2. 控制体重。经常锻炼可以帮助消耗多余的热量，从而帮助控制体重。此外，锻炼还可以增加肌肉质量，提高新陈代谢率，进一步帮助维持健康的体重。保持适当的体重有助于预防肥胖相关疾病，如心血管疾病和糖尿病。因此，通过坚持锻炼，可以更好地控制体重，保持身体健康。\n",
      "3. 经常锻炼可以提高免疫力，使身体更加抵抗疾病。锻炼可以促进血液循环，增加白细胞数量，从而帮助身体更快地应对病毒和细菌的入侵。此外，锻炼还可以减少患感冒和流感等呼吸道疾病的风险，让身体更加健康。\n",
      "4. 锻炼可以帮助改善睡眠质量，使人更容易入睡并保持深度睡眠状态。适当的运动可以调节身体的生物钟，帮助人们建立规律的作息时间。此外，锻炼也有助于减轻焦虑和压力，使人们在睡前更加放松，从而提高睡眠质量。良好的睡眠对身体健康和心理健康都至关重要。\n",
      "5. 经常锻炼可以减少患疾病的风险，包括心脏病、中风、糖尿病和某些癌症。运动有助于控制体重，减少肥胖引发的健康问题。此外，锻炼还可以提高身体的新陈代谢，促进身体内部的废物排出，保持身体健康。\n",
      "6. 锻炼可以释放身体内的内啡肽和多巴胺等神经递质，提升心情和情绪。这些化学物质可以帮助减轻焦虑和抑郁，让人感到更加愉快和放松。通过锻炼，人们可以更好地处理压力和情绪波动，保持心理健康。\n",
      "7. 锻炼还可以增加身体的灵活性和力量，减少关节疼痛和肌肉疲劳。此外，定期锻炼还可以改善身体姿势和平衡能力，降低摔倒和受伤的风险。最重要的是，锻炼可以提高自信心和自尊心，让人更加积极乐观地面对生活中的挑战。\n"
     ]
    }
   ],
   "source": [
    "llm = \"gpt-3.5-turbo-0125\"\n",
    "\n",
    "question_Chinese = \"经常锻炼有什么好处呢？\"\n",
    "final_result_Chinese = \"\"\n",
    "\n",
    "# For 循环顺序执行\n",
    "# for point_index, key_point in key_point_skeleton_Chinese:\n",
    "#     point_expanding_prompt = f\"\"\"您有责任在下面问题的总答案中续写一个且仅一个要点。\n",
    "\n",
    "# {question_Chinese}\n",
    "\n",
    "# 答案的骨架是：\n",
    "# {skeleton_result_Chinese}\n",
    "\n",
    "# 继续且仅继续书写点 {point_index}。用 3∼6 句话准确、简洁地写完，不要续写其他要点！\n",
    "# \"\"\"\n",
    "#     point_expanding = get_completions(point_expanding_prompt, llm)\n",
    "#     if point_index == key_point_skeleton[-1][0]:\n",
    "#         final_result_Chinese += f\"{point_index}. {point_expanding}\"\n",
    "#     else:\n",
    "#         final_result_Chinese += f\"{point_index}. {point_expanding}\\n\"\n",
    "\n",
    "# 使用 ThreadPoolExecutor 来并发执行 expand_point_Chinese 函数\n",
    "results = []\n",
    "max_workers = len(key_point_skeleton_Chinese)\n",
    "with ThreadPoolExecutor(max_workers=max_workers) as executor:\n",
    "    results = list(executor.map(expand_point_Chinese, key_point_skeleton_Chinese))\n",
    "\n",
    "for point_index, point_expanding in results:\n",
    "    # print(f\"Point Index: {point_index}, Point Expanding: {point_expanding}\")\n",
    "    if point_expanding[0].isnumeric():      # 判断有没有数字编号\n",
    "        pass\n",
    "    else:\n",
    "        point_expanding = f\"{point_index}. {point_expanding}\"\n",
    "    if point_index == key_point_skeleton_Chinese[-1][0]:\n",
    "        final_result_Chinese += f\"{point_expanding}\"\n",
    "    else:\n",
    "        final_result_Chinese += f\"{point_expanding}\\n\"\n",
    "\n",
    "print(f\"要点扩展后的内容-中文：\\n{final_result_Chinese}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "79c06079-9559-4f41-9eb8-862dc46be387",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "要点扩展后的内容-英文：\n",
      "1. Regular exercise helps with weight management by burning calories and increasing metabolism. It can also help build muscle mass, which in turn can help increase the body's ability to burn calories even at rest. Additionally, exercise can help regulate appetite and improve overall body composition.\n",
      "2. Regular exercise has been shown to improve mood by releasing endorphins, which are known as \"feel-good\" hormones. This can help reduce feelings of anxiety, stress, and depression, leading to a more positive outlook on life. Additionally, exercise can provide a sense of accomplishment and boost self-esteem, further enhancing overall emotional well-being.\n",
      "3. Regular exercise can lead to increased energy levels by improving cardiovascular health, increasing muscle strength, and enhancing overall endurance. This boost in energy can help individuals feel more alert and focused throughout the day, leading to increased productivity and a greater sense of well-being.\n",
      "4. Regular exercise can lead to better sleep quality by helping to regulate sleep patterns and improve overall sleep efficiency. Physical activity can also reduce symptoms of insomnia and promote deeper, more restful sleep. Additionally, exercise has been shown to decrease the time it takes to fall asleep and increase the amount of time spent in the restorative stages of sleep.\n",
      "5. Regular exercise helps to strengthen the immune system by promoting healthy circulation, which allows immune cells to move through the body more efficiently. This can help the body fight off infections and illnesses more effectively, reducing the frequency and severity of colds, flu, and other common ailments. Additionally, exercise can also help to reduce inflammation in the body, further supporting immune function.\n",
      "6. Regular exercise can help reduce the risk of chronic diseases such as heart disease, diabetes, and certain types of cancer. It can also improve cardiovascular health, lower blood pressure, and decrease the likelihood of developing conditions like osteoporosis. Additionally, exercise can help manage and improve symptoms of existing chronic diseases, leading to a better quality of life.\n",
      "7. Regular exercise has been shown to improve cognitive function by increasing blood flow to the brain, promoting the growth of new brain cells, and enhancing overall brain health. This can lead to better memory, sharper focus, and improved decision-making skills, ultimately contributing to a higher quality of life.\n",
      "8. Regular exercise also helps to improve overall physical health by strengthening the cardiovascular system, increasing muscle strength and endurance, improving flexibility and balance, and enhancing overall physical performance. This can lead to a decreased risk of injury, improved posture, and a greater ability to perform daily tasks with ease. Additionally, exercise can help to maintain bone density and reduce the risk of osteoporosis as we age.\n"
     ]
    }
   ],
   "source": [
    "llm = \"gpt-3.5-turbo-0125\"\n",
    "\n",
    "question_English = \"What are the benefits of regular exercise?\"\n",
    "final_result_English = \"\"\n",
    "\n",
    "# For 循环顺序执行\n",
    "# for point_index, key_point in key_point_skeleton_English:\n",
    "#     point_expanding_prompt = f\"\"\"You are responsible for continuing the writing of one and only one point \\\n",
    "# in the overall answer to the following question.\n",
    "\n",
    "# {question_English}\n",
    "\n",
    "# The skeleton of the answer is:\n",
    "# {skeleton_result_English}\n",
    "\n",
    "# Continue and only continue the writing of point {point_index}. \\\n",
    "# Write it accurately and concisely in 3∼5 sentences, and do not continue with other points!\n",
    "# \"\"\"\n",
    "#     point_expanding = get_completions(point_expanding_prompt, llm)\n",
    "#     if point_index == key_point_skeleton[-1][0]:\n",
    "#         final_result_English += f\"{point_index}. {point_expanding}\"\n",
    "#     else:\n",
    "#         final_result_English += f\"{point_index}. {point_expanding}\\n\"\n",
    "\n",
    "# 使用 ThreadPoolExecutor 来并发执行 expand_point_English 函数\n",
    "results = []\n",
    "max_workers = len(key_point_skeleton_English)\n",
    "with ThreadPoolExecutor(max_workers=4) as executor:\n",
    "    results = list(executor.map(expand_point_English, key_point_skeleton_English))\n",
    "\n",
    "for point_index, point_expanding in results:\n",
    "    # print(f\"Point Index: {point_index}, Point Expanding: {point_expanding}\")\n",
    "    if point_expanding[0].isnumeric():      # 判断有没有数字编号\n",
    "        pass\n",
    "    else:\n",
    "        point_expanding = f\"{point_index}. {point_expanding}\"\n",
    "    if point_index == key_point_skeleton_English[-1][0]:\n",
    "        final_result_English += f\"{point_expanding}\"\n",
    "    else:\n",
    "        final_result_English += f\"{point_expanding}\\n\"\n",
    "\n",
    "print(f\"要点扩展后的内容-英文：\\n{final_result_English}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aead6ef-82d8-484d-b720-36d0da3341a5",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2.3 骨架提示和要点展开合并模板"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b7db998-8adb-40fb-8746-961da445c653",
   "metadata": {},
   "source": [
    "思维骨架 (SoT) 方法通常使用两步提示：一是骨架提示，二是要点展开。\n",
    "\n",
    "实际上，我们可以通过一次性提示来合并这两步：首先构建思维骨架，然后填充它。只需更新问题并执行提示即可！这其实是在利用上下文学习（In-Context Learning）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f81a8b99-1cd9-4abc-8e31-d81cd9715123",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SoT生成结果-中文：\n",
      "骨架：\n",
      "1. 坚持每天阅读相关文献。\n",
      "2. 定期参加学术会议和讨论。\n",
      "3. 维护良好的数据管理习惯。\n",
      "4. 培养批判性思维和解决问题能力。\n",
      "5. 持续学习和掌握新技术和方法。\n",
      "6. 建立合作和交流的良好习惯。\n",
      "\n",
      "要点扩展后的答案：\n",
      "1. 养成每天阅读相关文献的习惯，可以帮助研究生及时了解最新的研究进展和成果，拓展自己的知识面，为自己的研究提供更多的参考和灵感。\n",
      "2. 定期参加学术会议和讨论，可以与同行交流学术观点、分享研究成果，拓展人脉关系，获得反馈和建议，促进自己的学术成长。\n",
      "3. 维护良好的数据管理习惯，包括数据备份、整理和保护，可以确保研究数据的完整性和可靠性，避免数据丢失或泄露的风险。\n",
      "4. 培养批判性思维和解决问题能力，可以帮助研究生在研究中发现问题、分析问题、解决问题，提高独立思考和创新能力。\n",
      "5. 持续学习和掌握新技术和方法，可以跟上科技发展的步伐，提高研究的效率和水平，为未来的科研工作打下坚实的基础。\n",
      "6. 建立合作和交流的良好习惯，可以促进团队合作，共同攻克科研难题，拓展研究领域，提高学术影响力。\n"
     ]
    }
   ],
   "source": [
    "llm = \"gpt-3.5-turbo-0125\"\n",
    "\n",
    "question_Chinese = \"人工智能专业的研究生应该养成那些良好的科研习惯？\"\n",
    "\n",
    "prompt_SoT_Chinese = f\"\"\"您的任务是分两步回答下面的问题。\\\n",
    "首先，用一个要点列表（编号为 1.、2.、3.等）列出简明扼要的答案要点。\\\n",
    "每个要点应非常简短，只有 2∼8 个字。一般来说，骨架应包含 3∼10 个要点。\\\n",
    "提供骨架后，用 3∼6 句话简要扩展每个要点。\n",
    "\n",
    "示例问题：\n",
    "经常锻炼有什么好处呢？\n",
    "骨架：\n",
    "1. 增强心肺功能。\n",
    "2. 控制体重。\n",
    "3. 提高免疫力。\n",
    "4. 改善睡眠质量。\n",
    "5. 减少患疾病风险。\n",
    "6. 提升心情和情绪。\n",
    "7. 延缓衰老。\n",
    "要点扩展后的答案：\n",
    "1. 经常锻炼可以增强心肺功能，使心脏更加强壮，提高肺活量，增加氧气的吸收量。这有助于提高身体的耐力和抵抗力，减少疲劳感，让我们在日常生活中更加精力充沛和活力十足。\n",
    "2. 控制体重。经常锻炼可以帮助消耗多余的热量，从而帮助控制体重。此外，锻炼还可以增加肌肉质量，提高新陈代谢率，进一步帮助维持健康的体重。保持适当的体重有助于预防肥胖相关疾病，如心血管疾病和糖尿病。因此，通过坚持锻炼，可以更好地控制体重，保持身体健康。\n",
    "3. 经常锻炼可以提高免疫力，使身体更加抵抗疾病。锻炼可以促进血液循环，增加白细胞数量，从而帮助身体更快地应对病毒和细菌的入侵。此外，锻炼还可以减少患感冒和流感等呼吸道疾病的风险，让身体更加健康。\n",
    "4. 锻炼可以帮助改善睡眠质量，使人更容易入睡并保持深度睡眠状态。适当的运动可以调节身体的生物钟，帮助人们建立规律的作息时间。此外，锻炼也有助于减轻焦虑和压力，使人们在睡前更加放松，从而提高睡眠质量。良好的睡眠对身体健康和心理健康都至关重要。\n",
    "5. 经常锻炼可以减少患疾病的风险，包括心脏病、中风、糖尿病和某些癌症。运动有助于控制体重，减少肥胖引发的健康问题。此外，锻炼还可以提高身体的新陈代谢，促进身体内部的废物排出，保持身体健康。\n",
    "6. 锻炼可以释放身体内的内啡肽和多巴胺等神经递质，提升心情和情绪。这些化学物质可以帮助减轻焦虑和抑郁，让人感到更加愉快和放松。通过锻炼，人们可以更好地处理压力和情绪波动，保持心理健康。\n",
    "7. 锻炼还可以增加身体的灵活性和力量，减少关节疼痛和肌肉疲劳。此外，定期锻炼还可以改善身体姿势和平衡能力，降低摔倒和受伤的风险。最重要的是，锻炼可以提高自信心和自尊心，让人更加积极乐观地面对生活中的挑战。\n",
    "您的任务是回答：```{question_Chinese}```\n",
    "\n",
    "骨架：\n",
    "1. \n",
    "2. \n",
    "...\n",
    "\n",
    "要点扩展后的答案：\n",
    "1. \n",
    "2. \n",
    "...\n",
    "\"\"\"\n",
    "\n",
    "result_SoT_Chinese = get_completions(prompt_SoT_Chinese, llm)\n",
    "\n",
    "print(f\"SoT生成结果-中文：\\n{result_SoT_Chinese}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "89cde20c-a5e8-4274-a247-bd4d15e4b70c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SoT生成结果-英文：\n",
      "Skeleton:\n",
      "1. Stay organized.\n",
      "2. Develop critical thinking skills.\n",
      "3. Stay updated on current research.\n",
      "4. Collaborate with peers and experts.\n",
      "5. Seek feedback and constructive criticism.\n",
      "6. Practice time management.\n",
      "7. Utilize a variety of resources.\n",
      "8. Keep detailed notes.\n",
      "9. Develop strong writing skills.\n",
      "\n",
      "Expanded Answer:\n",
      "1. Graduate students in Artificial Intelligence should prioritize staying organized to keep track of research papers, deadlines, and project progress. This can help them stay focused and efficient in their work, leading to better outcomes.\n",
      "2. Developing critical thinking skills is essential for graduate students in AI to analyze research findings, identify gaps in knowledge, and propose innovative solutions. This can help them contribute meaningfully to the field and advance their own research.\n",
      "3. It is important for graduate students to stay updated on current research trends, breakthroughs, and technologies in AI to ensure they are working on cutting-edge projects and contributing to the field's advancement.\n",
      "4. Collaborating with peers and experts in the field can provide valuable insights, feedback, and perspectives that can enhance research projects and lead to new discoveries. This can also help students build a strong network of professionals in the AI community.\n",
      "5. Seeking feedback and constructive criticism from advisors, mentors, and peers can help graduate students improve their research skills, identify areas for growth, and refine their work to meet high standards.\n",
      "6. Practice time management is crucial for graduate students to balance coursework, research projects, and personal life effectively. This can help them meet deadlines, avoid burnout, and maintain a healthy work-life balance.\n",
      "7. Utilizing a variety of resources such as academic journals, conferences, workshops, and online platforms can provide graduate students with diverse perspectives, methodologies, and tools to enhance their research projects.\n",
      "8. Keeping detailed notes of research findings, methodologies, and experiments can help graduate students track their progress, replicate results, and maintain accurate records for future reference.\n",
      "9. Developing strong writing skills is essential for graduate students to communicate their research findings, methodologies, and conclusions effectively in academic papers, presentations, and thesis documents. This can help them convey complex ideas clearly and contribute meaningfully to the AI research community.\n"
     ]
    }
   ],
   "source": [
    "llm = \"gpt-3.5-turbo-0125\"\n",
    "\n",
    "question_English = \"What good research habits should graduate students in Artificial Intelligence Major?\"\n",
    "\n",
    "prompt_SoT_English = f\"\"\"You are tasked with answering the following question in a two-step process. \\\n",
    "First, create a concise skeleton of the answer in a list of points (numbered 1., 2., 3., etc.). \\\n",
    "Each skeleton point should be very short with only 2~6 words. \\\n",
    "Generally, the skeleton should have 3∼10 points. \\\n",
    "After providing the skeleton, expand on each point briefly in 2∼4 sentences.\n",
    "\n",
    "Example Question:\n",
    "What are the benefits of regular exercise?\n",
    "Skeleton:\n",
    "1. Weight management.\n",
    "2. Improved mood.\n",
    "3. Increased energy levels.\n",
    "4. Better sleep quality.\n",
    "5. Stronger immune system.\n",
    "6. Reduced risk of chronic diseases.\n",
    "7. Improved cognitive function.\n",
    "8. Better overall physical health.\n",
    "Expanded Answer:\n",
    "1. Regular exercise helps with weight management by burning calories and increasing metabolism. It can also help build muscle mass, which in turn can help increase the body's ability to burn calories even at rest. Additionally, exercise can help regulate appetite and improve overall body composition.\n",
    "2. Regular exercise has been shown to improve mood by releasing endorphins, which are known as \"feel-good\" hormones. This can help reduce feelings of anxiety, stress, and depression, leading to a more positive outlook on life. Additionally, exercise can provide a sense of accomplishment and boost self-esteem, further enhancing overall emotional well-being.\n",
    "3. Regular exercise can lead to increased energy levels by improving cardiovascular health, increasing muscle strength, and enhancing overall endurance. This boost in energy can help individuals feel more alert and focused throughout the day, leading to increased productivity and a greater sense of well-being.\n",
    "4. Regular exercise can lead to better sleep quality by helping to regulate sleep patterns and improve overall sleep efficiency. Physical activity can also reduce symptoms of insomnia and promote deeper, more restful sleep. Additionally, exercise has been shown to decrease the time it takes to fall asleep and increase the amount of time spent in the restorative stages of sleep.\n",
    "5. Regular exercise helps to strengthen the immune system by promoting healthy circulation, which allows immune cells to move through the body more efficiently. This can help the body fight off infections and illnesses more effectively, reducing the frequency and severity of colds, flu, and other common ailments. Additionally, exercise can also help to reduce inflammation in the body, further supporting immune function.\n",
    "6. Regular exercise can help reduce the risk of chronic diseases such as heart disease, diabetes, and certain types of cancer. It can also improve cardiovascular health, lower blood pressure, and decrease the likelihood of developing conditions like osteoporosis. Additionally, exercise can help manage and improve symptoms of existing chronic diseases, leading to a better quality of life.\n",
    "7. Regular exercise has been shown to improve cognitive function by increasing blood flow to the brain, promoting the growth of new brain cells, and enhancing overall brain health. This can lead to better memory, sharper focus, and improved decision-making skills, ultimately contributing to a higher quality of life.\n",
    "8. Regular exercise also helps to improve overall physical health by strengthening the cardiovascular system, increasing muscle strength and endurance, improving flexibility and balance, and enhancing overall physical performance. This can lead to a decreased risk of injury, improved posture, and a greater ability to perform daily tasks with ease. Additionally, exercise can help to maintain bone density and reduce the risk of osteoporosis as we age.\n",
    "Your task is to answer: ```{question_English}```\n",
    "\n",
    "Skeleton:\n",
    "1. \n",
    "2. \n",
    "...\n",
    "\n",
    "Expanded Answer:\n",
    "1. \n",
    "2. \n",
    "...\n",
    "\"\"\"\n",
    "\n",
    "result_SoT_English = get_completions(prompt_SoT_English, llm)\n",
    "\n",
    "print(f\"SoT生成结果-英文：\\n{result_SoT_English}\")"
   ]
  },
  {
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"
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    "## 三、总结与讨论\n",
    "***\n",
    "\n",
    "**要点总结**：\n",
    "- SoT 的核心思想是先引导 LLMs 生成答案的骨架，然后并行地对每个骨架点进行扩展，从而实现并行解码，有效避免了顺序解码的高延迟。\n",
    "\n",
    "- SoT 是一种数据层面的优化技术，利用了 LLMs 的指令遵循和规划能力，展示了通过显式规划答案结构来促进获得高质量答案的潜力。\n",
    "\n",
    "- SoT 已在 12 个最新的 LLMs 上进行了测试，结果显示 SoT 不仅能显著提高生成速度（最高达 2.39 倍，有效降低 LLMs 的生成延迟），而且在某些情况下还能提高答案质量。\n",
    "\n",
    "![思维骨架_图解.png](attachment:b9aa8813-523e-4b9d-a5a5-84098da075f5.png)\n",
    "\n",
    "\n",
    "📚 主要参考资料：\n",
    "- ICLR 2024 - Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding，https://openreview.net/forum?id=mqVgBbNCm9\n",
    "\n",
    "- 大模型速度狂飙 2.39 倍！清华联手微软首提 SoT，让 LLM 思考更像人类，https://mp.weixin.qq.com/s/9t1opfhUYm3yJuEoKPvVuQ，\n",
    "\n",
    "- 思维骨架 SoT 如何提升 LLM 的速度？丨论文解读，https://juejin.cn/post/7269607286195699727"
   ]
  }
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